Early Alert System in Online Education

University Research Chair, Dr. Scott WM Burrus, and Research Fellows Dr. Greg Bradley, Dr. Meena Clowes, and Dr. Helen-Zaikina Montgomery, along with Research Affiliates Dr. Meryl Epstein and Dr. Elizabeth Young from the Center for Learning Analytics Research are excited to announce their acceptance into the Distance Learning Administration Conference hosted by the University of West Georgia DLA2017 annually held at the Jekyll Island, Georgia Conference Center.  The CLAR team will be submitting four papers covering critical areas that focus on the Center’s research agenda and include:

Reassessing the scale reliability of the SmarterMeasure 

Assessing the predictive validity of the Smarter Measure

Early Alert System in Online Education

Comparative Typology of Student and Institutional Expectations of Online Faculty.

Abstract: 

Early Alert System for Online Students

 

Globally and across the United States distance learning administrators are increasingly attuned to the importance of student persistence and retention initiatives and innovations.  One innovation that appears to be widely implemented is a system that provides a link between online faculty and academic advisors with the intent to intervene with students at risk for early program attrition. One such intervention that has been proposed in academic and practitioner literature is an Early Alert System (EAS) (Allen, et al., 2016; Cai, Lewis, & Higdon, 2015).  Little is known about the characteristics of those students likely to obtain an Early Alert.                                                                

 

                                                        Student Profile

 

Demographically, students who take online courses differ from peers who take courses in more traditional settings (Ortagus, 2017). To improve student retention and graduation rates, online universities need to develop and advance effective interventions specific to at-risk students in online classes. To offer effective interventions to students in online courses, these interventions need to be well-matched to the online learning environment (Allen, et al., 2016; Donnelly, 2010; McElroy & Lubich, 2013) as well as to the demographic characteristics of those who are most likely to struggle in their course work (Ortagus, 2017).  This study was an exploration of the descriptive characteristics of students that received an Early Alert in a sample of courses at a large predominately online university in the United States. 

 

                                                               Research Questions

 

The following questions guided this research:

1.      What is the demographic profile of students who received an Early Alert?

2.      How to students on who received an Early Alert differ demographically from students who did not receive an Early Alert?

                                                        

                                                          Conclusions

 

The results of this study indicate that students who receive an EA tend to be younger, have a lower GPA, and fewer transfer credits. These students also appear to be more likely to withdraw from a course in which they encounter difficulties. These findings suggest that students in this sample who received an EA tend to be more academically “at risk.” Surprisingly, EA status was not associated with those demographic characteristics that would be considered to add more responsibilities outside of school, such as the number of dependents a student has and were associated with higher incidence of EA among single than married students.  Females were also more likely to receive EA than males. While many hypothetical explanations can be put forth regarding the demographics of “at risk” students who’ve received an EA, any demographic analyses should be interpreted with caution.  Additional data and further analysis, including parametric statistics, and hierarchical linear modeling to account for potential nested effects should be conducted before conclusions are drawn about the extent to which demographics may predict early course persistence.

 

This publication has been peer reviewed.
Publication Type: 
Journal Article
Authors: 
Dr. Scott WM Burrus, and Research Fellows Dr. Greg Bradley, Dr. Meena Clowes, and Dr. Helen-Zaikina Montgomery, along with Research Affiliates Dr. Meryl Epstein and Dr. Elizabeth Young
Year of Publication: 
2017
Journal, Book, Magazine or Other Publication Title: 
DLA
Publication Language: 
English
Boyer's Domain: 

Additional content will be provided upon request.

Elizabeth Young

Additional content will be provided upon request.

Elizabeth Young
More posts by author: